Senior AI Trainer

Role Title: Senior AI Trainer

Role Type: Contractor, Remote

Location: Northern America and Europe.

micro1 is engaging Senior AI Trainers to collaborate on a customer-driven project enhancing AI system quality through expert-level model training and evaluation. In this role, you'll apply your generalist expertise and hands-on experience with AI products to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.

Key Responsibilities:

1. Evaluate the performance of AI chat and search tools under standardized, repeatable conditions.
2. Apply detailed grading guidelines to accurately evaluate, rank, and annotate model responses, providing structured feedback that improves model performance.
3. Write, refine, and stress-test prompts that probe model capabilities, ensuring coverage of diverse scenarios, edge cases, and user intents.
4. Verify consistency and accuracy across evaluations, correcting discrepancies between ratings and the underlying rubric or guidelines.
5. Utilize AI training tools and platforms to record findings and submit evaluation datasets in alignment with project standards and milestones.
6. Collaborate with project trainers and contributors to resolve ambiguities, refine guidelines, and ensure evaluation consistency across the team.
7. Participate in ongoing quality reviews, incorporating feedback to maintain rigorous training and annotation standards.

Required Skills and Qualifications:

1. Exceptional attention to detail and accuracy in reviewing and evaluating AI-generated content.
2. Strong written and verbal communication skills for effective collaboration, reporting, and timely updates on progress and challenges.
3. Demonstrated time management and self-organization abilities for independent, remote project participation.
4. Analytical and problem-solving mindset, with a proactive approach to resolving evaluation challenges.

Preferred Qualifications:

1. Proven experience with AI model training, RLHF, data labeling, or similar data-centric evaluation projects.
2. Familiarity with AI products and tools, including prompt engineering and understanding of model behaviors and limitations.
3. Experience writing evaluation rubrics, grading model outputs, or reviewing AI responses for accuracy, helpfulness, and safety.
4. Background in AI training, machine learning data preparation, or LLM-focused projects (strongly preferred).

Originally posted on Himalayas